Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Wednesday 22 July 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 184,059 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 184,049 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 7
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 0
## 144 2020-03-01 London 0
## 145 2020-03-02 London 0
## 146 2020-03-03 London 0
## 147 2020-03-04 London 0
## 148 2020-03-05 London 0
## 149 2020-03-06 London 1
## 150 2020-03-07 London 0
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## 288 2020-03-02 Midlands 0
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## 293 2020-03-07 Midlands 0
## 294 2020-03-08 Midlands 2
## 295 2020-03-09 Midlands 1
## 296 2020-03-10 Midlands 0
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## 298 2020-03-12 Midlands 6
## 299 2020-03-13 Midlands 5
## 300 2020-03-14 Midlands 4
## 301 2020-03-15 Midlands 5
## 302 2020-03-16 Midlands 11
## 303 2020-03-17 Midlands 8
## 304 2020-03-18 Midlands 13
## 305 2020-03-19 Midlands 8
## 306 2020-03-20 Midlands 28
## 307 2020-03-21 Midlands 13
## 308 2020-03-22 Midlands 31
## 309 2020-03-23 Midlands 33
## 310 2020-03-24 Midlands 41
## 311 2020-03-25 Midlands 48
## 312 2020-03-26 Midlands 64
## 313 2020-03-27 Midlands 72
## 314 2020-03-28 Midlands 89
## 315 2020-03-29 Midlands 92
## 316 2020-03-30 Midlands 90
## 317 2020-03-31 Midlands 123
## 318 2020-04-01 Midlands 140
## 319 2020-04-02 Midlands 142
## 320 2020-04-03 Midlands 124
## 321 2020-04-04 Midlands 151
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## 324 2020-04-07 Midlands 123
## 325 2020-04-08 Midlands 186
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## 342 2020-04-25 Midlands 72
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## 347 2020-04-30 Midlands 56
## 348 2020-05-01 Midlands 64
## 349 2020-05-02 Midlands 51
## 350 2020-05-03 Midlands 52
## 351 2020-05-04 Midlands 61
## 352 2020-05-05 Midlands 59
## 353 2020-05-06 Midlands 59
## 354 2020-05-07 Midlands 48
## 355 2020-05-08 Midlands 34
## 356 2020-05-09 Midlands 37
## 357 2020-05-10 Midlands 42
## 358 2020-05-11 Midlands 33
## 359 2020-05-12 Midlands 45
## 360 2020-05-13 Midlands 40
## 361 2020-05-14 Midlands 38
## 362 2020-05-15 Midlands 40
## 363 2020-05-16 Midlands 34
## 364 2020-05-17 Midlands 31
## 365 2020-05-18 Midlands 36
## 366 2020-05-19 Midlands 35
## 367 2020-05-20 Midlands 36
## 368 2020-05-21 Midlands 32
## 369 2020-05-22 Midlands 27
## 370 2020-05-23 Midlands 34
## 371 2020-05-24 Midlands 20
## 372 2020-05-25 Midlands 26
## 373 2020-05-26 Midlands 33
## 374 2020-05-27 Midlands 29
## 375 2020-05-28 Midlands 28
## 376 2020-05-29 Midlands 20
## 377 2020-05-30 Midlands 21
## 378 2020-05-31 Midlands 22
## 379 2020-06-01 Midlands 20
## 380 2020-06-02 Midlands 22
## 381 2020-06-03 Midlands 24
## 382 2020-06-04 Midlands 16
## 383 2020-06-05 Midlands 21
## 384 2020-06-06 Midlands 20
## 385 2020-06-07 Midlands 17
## 386 2020-06-08 Midlands 16
## 387 2020-06-09 Midlands 18
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## 391 2020-06-13 Midlands 6
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## 424 2020-07-16 Midlands 2
## 425 2020-07-17 Midlands 1
## 426 2020-07-18 Midlands 2
## 427 2020-07-19 Midlands 2
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## 429 2020-07-21 Midlands 0
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## 431 2020-03-02 North East and Yorkshire 0
## 432 2020-03-03 North East and Yorkshire 0
## 433 2020-03-04 North East and Yorkshire 0
## 434 2020-03-05 North East and Yorkshire 0
## 435 2020-03-06 North East and Yorkshire 0
## 436 2020-03-07 North East and Yorkshire 0
## 437 2020-03-08 North East and Yorkshire 0
## 438 2020-03-09 North East and Yorkshire 0
## 439 2020-03-10 North East and Yorkshire 0
## 440 2020-03-11 North East and Yorkshire 0
## 441 2020-03-12 North East and Yorkshire 0
## 442 2020-03-13 North East and Yorkshire 0
## 443 2020-03-14 North East and Yorkshire 0
## 444 2020-03-15 North East and Yorkshire 2
## 445 2020-03-16 North East and Yorkshire 3
## 446 2020-03-17 North East and Yorkshire 1
## 447 2020-03-18 North East and Yorkshire 2
## 448 2020-03-19 North East and Yorkshire 6
## 449 2020-03-20 North East and Yorkshire 5
## 450 2020-03-21 North East and Yorkshire 6
## 451 2020-03-22 North East and Yorkshire 7
## 452 2020-03-23 North East and Yorkshire 9
## 453 2020-03-24 North East and Yorkshire 8
## 454 2020-03-25 North East and Yorkshire 18
## 455 2020-03-26 North East and Yorkshire 21
## 456 2020-03-27 North East and Yorkshire 28
## 457 2020-03-28 North East and Yorkshire 35
## 458 2020-03-29 North East and Yorkshire 38
## 459 2020-03-30 North East and Yorkshire 64
## 460 2020-03-31 North East and Yorkshire 60
## 461 2020-04-01 North East and Yorkshire 67
## 462 2020-04-02 North East and Yorkshire 75
## 463 2020-04-03 North East and Yorkshire 100
## 464 2020-04-04 North East and Yorkshire 105
## 465 2020-04-05 North East and Yorkshire 92
## 466 2020-04-06 North East and Yorkshire 96
## 467 2020-04-07 North East and Yorkshire 102
## 468 2020-04-08 North East and Yorkshire 107
## 469 2020-04-09 North East and Yorkshire 111
## 470 2020-04-10 North East and Yorkshire 117
## 471 2020-04-11 North East and Yorkshire 98
## 472 2020-04-12 North East and Yorkshire 84
## 473 2020-04-13 North East and Yorkshire 94
## 474 2020-04-14 North East and Yorkshire 107
## 475 2020-04-15 North East and Yorkshire 96
## 476 2020-04-16 North East and Yorkshire 103
## 477 2020-04-17 North East and Yorkshire 88
## 478 2020-04-18 North East and Yorkshire 95
## 479 2020-04-19 North East and Yorkshire 88
## 480 2020-04-20 North East and Yorkshire 100
## 481 2020-04-21 North East and Yorkshire 76
## 482 2020-04-22 North East and Yorkshire 84
## 483 2020-04-23 North East and Yorkshire 63
## 484 2020-04-24 North East and Yorkshire 72
## 485 2020-04-25 North East and Yorkshire 69
## 486 2020-04-26 North East and Yorkshire 65
## 487 2020-04-27 North East and Yorkshire 65
## 488 2020-04-28 North East and Yorkshire 57
## 489 2020-04-29 North East and Yorkshire 69
## 490 2020-04-30 North East and Yorkshire 57
## 491 2020-05-01 North East and Yorkshire 64
## 492 2020-05-02 North East and Yorkshire 48
## 493 2020-05-03 North East and Yorkshire 40
## 494 2020-05-04 North East and Yorkshire 49
## 495 2020-05-05 North East and Yorkshire 40
## 496 2020-05-06 North East and Yorkshire 51
## 497 2020-05-07 North East and Yorkshire 45
## 498 2020-05-08 North East and Yorkshire 42
## 499 2020-05-09 North East and Yorkshire 44
## 500 2020-05-10 North East and Yorkshire 40
## 501 2020-05-11 North East and Yorkshire 29
## 502 2020-05-12 North East and Yorkshire 27
## 503 2020-05-13 North East and Yorkshire 28
## 504 2020-05-14 North East and Yorkshire 31
## 505 2020-05-15 North East and Yorkshire 32
## 506 2020-05-16 North East and Yorkshire 35
## 507 2020-05-17 North East and Yorkshire 26
## 508 2020-05-18 North East and Yorkshire 30
## 509 2020-05-19 North East and Yorkshire 27
## 510 2020-05-20 North East and Yorkshire 22
## 511 2020-05-21 North East and Yorkshire 33
## 512 2020-05-22 North East and Yorkshire 22
## 513 2020-05-23 North East and Yorkshire 18
## 514 2020-05-24 North East and Yorkshire 26
## 515 2020-05-25 North East and Yorkshire 21
## 516 2020-05-26 North East and Yorkshire 21
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## 519 2020-05-29 North East and Yorkshire 25
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## 531 2020-06-10 North East and Yorkshire 19
## 532 2020-06-11 North East and Yorkshire 7
## 533 2020-06-12 North East and Yorkshire 9
## 534 2020-06-13 North East and Yorkshire 10
## 535 2020-06-14 North East and Yorkshire 11
## 536 2020-06-15 North East and Yorkshire 9
## 537 2020-06-16 North East and Yorkshire 10
## 538 2020-06-17 North East and Yorkshire 9
## 539 2020-06-18 North East and Yorkshire 11
## 540 2020-06-19 North East and Yorkshire 6
## 541 2020-06-20 North East and Yorkshire 5
## 542 2020-06-21 North East and Yorkshire 4
## 543 2020-06-22 North East and Yorkshire 6
## 544 2020-06-23 North East and Yorkshire 7
## 545 2020-06-24 North East and Yorkshire 10
## 546 2020-06-25 North East and Yorkshire 4
## 547 2020-06-26 North East and Yorkshire 7
## 548 2020-06-27 North East and Yorkshire 3
## 549 2020-06-28 North East and Yorkshire 5
## 550 2020-06-29 North East and Yorkshire 2
## 551 2020-06-30 North East and Yorkshire 5
## 552 2020-07-01 North East and Yorkshire 1
## 553 2020-07-02 North East and Yorkshire 4
## 554 2020-07-03 North East and Yorkshire 3
## 555 2020-07-04 North East and Yorkshire 4
## 556 2020-07-05 North East and Yorkshire 2
## 557 2020-07-06 North East and Yorkshire 2
## 558 2020-07-07 North East and Yorkshire 3
## 559 2020-07-08 North East and Yorkshire 3
## 560 2020-07-09 North East and Yorkshire 0
## 561 2020-07-10 North East and Yorkshire 3
## 562 2020-07-11 North East and Yorkshire 1
## 563 2020-07-12 North East and Yorkshire 4
## 564 2020-07-13 North East and Yorkshire 1
## 565 2020-07-14 North East and Yorkshire 1
## 566 2020-07-15 North East and Yorkshire 2
## 567 2020-07-16 North East and Yorkshire 2
## 568 2020-07-17 North East and Yorkshire 1
## 569 2020-07-18 North East and Yorkshire 2
## 570 2020-07-19 North East and Yorkshire 2
## 571 2020-07-20 North East and Yorkshire 1
## 572 2020-07-21 North East and Yorkshire 0
## 573 2020-03-01 North West 0
## 574 2020-03-02 North West 0
## 575 2020-03-03 North West 0
## 576 2020-03-04 North West 0
## 577 2020-03-05 North West 1
## 578 2020-03-06 North West 0
## 579 2020-03-07 North West 0
## 580 2020-03-08 North West 1
## 581 2020-03-09 North West 0
## 582 2020-03-10 North West 0
## 583 2020-03-11 North West 0
## 584 2020-03-12 North West 2
## 585 2020-03-13 North West 3
## 586 2020-03-14 North West 1
## 587 2020-03-15 North West 4
## 588 2020-03-16 North West 2
## 589 2020-03-17 North West 4
## 590 2020-03-18 North West 6
## 591 2020-03-19 North West 7
## 592 2020-03-20 North West 10
## 593 2020-03-21 North West 11
## 594 2020-03-22 North West 13
## 595 2020-03-23 North West 15
## 596 2020-03-24 North West 21
## 597 2020-03-25 North West 21
## 598 2020-03-26 North West 29
## 599 2020-03-27 North West 36
## 600 2020-03-28 North West 28
## 601 2020-03-29 North West 46
## 602 2020-03-30 North West 67
## 603 2020-03-31 North West 52
## 604 2020-04-01 North West 86
## 605 2020-04-02 North West 96
## 606 2020-04-03 North West 95
## 607 2020-04-04 North West 98
## 608 2020-04-05 North West 102
## 609 2020-04-06 North West 100
## 610 2020-04-07 North West 135
## 611 2020-04-08 North West 127
## 612 2020-04-09 North West 119
## 613 2020-04-10 North West 117
## 614 2020-04-11 North West 138
## 615 2020-04-12 North West 125
## 616 2020-04-13 North West 129
## 617 2020-04-14 North West 131
## 618 2020-04-15 North West 114
## 619 2020-04-16 North West 135
## 620 2020-04-17 North West 98
## 621 2020-04-18 North West 113
## 622 2020-04-19 North West 71
## 623 2020-04-20 North West 83
## 624 2020-04-21 North West 76
## 625 2020-04-22 North West 86
## 626 2020-04-23 North West 85
## 627 2020-04-24 North West 66
## 628 2020-04-25 North West 66
## 629 2020-04-26 North West 55
## 630 2020-04-27 North West 54
## 631 2020-04-28 North West 57
## 632 2020-04-29 North West 63
## 633 2020-04-30 North West 59
## 634 2020-05-01 North West 45
## 635 2020-05-02 North West 56
## 636 2020-05-03 North West 55
## 637 2020-05-04 North West 48
## 638 2020-05-05 North West 48
## 639 2020-05-06 North West 44
## 640 2020-05-07 North West 49
## 641 2020-05-08 North West 42
## 642 2020-05-09 North West 31
## 643 2020-05-10 North West 42
## 644 2020-05-11 North West 35
## 645 2020-05-12 North West 38
## 646 2020-05-13 North West 25
## 647 2020-05-14 North West 26
## 648 2020-05-15 North West 33
## 649 2020-05-16 North West 32
## 650 2020-05-17 North West 24
## 651 2020-05-18 North West 31
## 652 2020-05-19 North West 35
## 653 2020-05-20 North West 27
## 654 2020-05-21 North West 27
## 655 2020-05-22 North West 26
## 656 2020-05-23 North West 31
## 657 2020-05-24 North West 26
## 658 2020-05-25 North West 31
## 659 2020-05-26 North West 27
## 660 2020-05-27 North West 27
## 661 2020-05-28 North West 28
## 662 2020-05-29 North West 20
## 663 2020-05-30 North West 19
## 664 2020-05-31 North West 13
## 665 2020-06-01 North West 12
## 666 2020-06-02 North West 27
## 667 2020-06-03 North West 22
## 668 2020-06-04 North West 22
## 669 2020-06-05 North West 16
## 670 2020-06-06 North West 26
## 671 2020-06-07 North West 20
## 672 2020-06-08 North West 23
## 673 2020-06-09 North West 17
## 674 2020-06-10 North West 16
## 675 2020-06-11 North West 16
## 676 2020-06-12 North West 11
## 677 2020-06-13 North West 10
## 678 2020-06-14 North West 15
## 679 2020-06-15 North West 16
## 680 2020-06-16 North West 15
## 681 2020-06-17 North West 13
## 682 2020-06-18 North West 13
## 683 2020-06-19 North West 7
## 684 2020-06-20 North West 11
## 685 2020-06-21 North West 8
## 686 2020-06-22 North West 11
## 687 2020-06-23 North West 13
## 688 2020-06-24 North West 13
## 689 2020-06-25 North West 15
## 690 2020-06-26 North West 6
## 691 2020-06-27 North West 7
## 692 2020-06-28 North West 9
## 693 2020-06-29 North West 9
## 694 2020-06-30 North West 7
## 695 2020-07-01 North West 3
## 696 2020-07-02 North West 6
## 697 2020-07-03 North West 7
## 698 2020-07-04 North West 4
## 699 2020-07-05 North West 6
## 700 2020-07-06 North West 9
## 701 2020-07-07 North West 8
## 702 2020-07-08 North West 5
## 703 2020-07-09 North West 10
## 704 2020-07-10 North West 2
## 705 2020-07-11 North West 4
## 706 2020-07-12 North West 0
## 707 2020-07-13 North West 6
## 708 2020-07-14 North West 3
## 709 2020-07-15 North West 4
## 710 2020-07-16 North West 2
## 711 2020-07-17 North West 3
## 712 2020-07-18 North West 2
## 713 2020-07-19 North West 0
## 714 2020-07-20 North West 0
## 715 2020-07-21 North West 0
## 716 2020-03-01 South East 0
## 717 2020-03-02 South East 0
## 718 2020-03-03 South East 1
## 719 2020-03-04 South East 0
## 720 2020-03-05 South East 1
## 721 2020-03-06 South East 0
## 722 2020-03-07 South East 0
## 723 2020-03-08 South East 1
## 724 2020-03-09 South East 1
## 725 2020-03-10 South East 1
## 726 2020-03-11 South East 1
## 727 2020-03-12 South East 0
## 728 2020-03-13 South East 1
## 729 2020-03-14 South East 1
## 730 2020-03-15 South East 5
## 731 2020-03-16 South East 8
## 732 2020-03-17 South East 7
## 733 2020-03-18 South East 10
## 734 2020-03-19 South East 9
## 735 2020-03-20 South East 13
## 736 2020-03-21 South East 7
## 737 2020-03-22 South East 25
## 738 2020-03-23 South East 20
## 739 2020-03-24 South East 22
## 740 2020-03-25 South East 29
## 741 2020-03-26 South East 35
## 742 2020-03-27 South East 34
## 743 2020-03-28 South East 36
## 744 2020-03-29 South East 55
## 745 2020-03-30 South East 58
## 746 2020-03-31 South East 65
## 747 2020-04-01 South East 66
## 748 2020-04-02 South East 55
## 749 2020-04-03 South East 72
## 750 2020-04-04 South East 80
## 751 2020-04-05 South East 82
## 752 2020-04-06 South East 88
## 753 2020-04-07 South East 100
## 754 2020-04-08 South East 83
## 755 2020-04-09 South East 104
## 756 2020-04-10 South East 88
## 757 2020-04-11 South East 88
## 758 2020-04-12 South East 88
## 759 2020-04-13 South East 84
## 760 2020-04-14 South East 65
## 761 2020-04-15 South East 72
## 762 2020-04-16 South East 56
## 763 2020-04-17 South East 86
## 764 2020-04-18 South East 57
## 765 2020-04-19 South East 70
## 766 2020-04-20 South East 87
## 767 2020-04-21 South East 51
## 768 2020-04-22 South East 54
## 769 2020-04-23 South East 57
## 770 2020-04-24 South East 64
## 771 2020-04-25 South East 51
## 772 2020-04-26 South East 51
## 773 2020-04-27 South East 41
## 774 2020-04-28 South East 40
## 775 2020-04-29 South East 47
## 776 2020-04-30 South East 29
## 777 2020-05-01 South East 37
## 778 2020-05-02 South East 36
## 779 2020-05-03 South East 17
## 780 2020-05-04 South East 35
## 781 2020-05-05 South East 29
## 782 2020-05-06 South East 25
## 783 2020-05-07 South East 27
## 784 2020-05-08 South East 26
## 785 2020-05-09 South East 28
## 786 2020-05-10 South East 19
## 787 2020-05-11 South East 25
## 788 2020-05-12 South East 27
## 789 2020-05-13 South East 18
## 790 2020-05-14 South East 32
## 791 2020-05-15 South East 25
## 792 2020-05-16 South East 22
## 793 2020-05-17 South East 18
## 794 2020-05-18 South East 22
## 795 2020-05-19 South East 12
## 796 2020-05-20 South East 22
## 797 2020-05-21 South East 15
## 798 2020-05-22 South East 17
## 799 2020-05-23 South East 21
## 800 2020-05-24 South East 17
## 801 2020-05-25 South East 13
## 802 2020-05-26 South East 19
## 803 2020-05-27 South East 18
## 804 2020-05-28 South East 12
## 805 2020-05-29 South East 21
## 806 2020-05-30 South East 8
## 807 2020-05-31 South East 12
## 808 2020-06-01 South East 11
## 809 2020-06-02 South East 13
## 810 2020-06-03 South East 18
## 811 2020-06-04 South East 11
## 812 2020-06-05 South East 11
## 813 2020-06-06 South East 10
## 814 2020-06-07 South East 12
## 815 2020-06-08 South East 8
## 816 2020-06-09 South East 10
## 817 2020-06-10 South East 11
## 818 2020-06-11 South East 5
## 819 2020-06-12 South East 6
## 820 2020-06-13 South East 7
## 821 2020-06-14 South East 7
## 822 2020-06-15 South East 8
## 823 2020-06-16 South East 13
## 824 2020-06-17 South East 9
## 825 2020-06-18 South East 4
## 826 2020-06-19 South East 7
## 827 2020-06-20 South East 5
## 828 2020-06-21 South East 3
## 829 2020-06-22 South East 2
## 830 2020-06-23 South East 8
## 831 2020-06-24 South East 7
## 832 2020-06-25 South East 5
## 833 2020-06-26 South East 8
## 834 2020-06-27 South East 8
## 835 2020-06-28 South East 6
## 836 2020-06-29 South East 5
## 837 2020-06-30 South East 5
## 838 2020-07-01 South East 2
## 839 2020-07-02 South East 7
## 840 2020-07-03 South East 3
## 841 2020-07-04 South East 5
## 842 2020-07-05 South East 4
## 843 2020-07-06 South East 3
## 844 2020-07-07 South East 5
## 845 2020-07-08 South East 3
## 846 2020-07-09 South East 7
## 847 2020-07-10 South East 3
## 848 2020-07-11 South East 1
## 849 2020-07-12 South East 3
## 850 2020-07-13 South East 4
## 851 2020-07-14 South East 3
## 852 2020-07-15 South East 4
## 853 2020-07-16 South East 3
## 854 2020-07-17 South East 1
## 855 2020-07-18 South East 4
## 856 2020-07-19 South East 2
## 857 2020-07-20 South East 1
## 858 2020-07-21 South East 0
## 859 2020-03-01 South West 0
## 860 2020-03-02 South West 0
## 861 2020-03-03 South West 0
## 862 2020-03-04 South West 0
## 863 2020-03-05 South West 0
## 864 2020-03-06 South West 0
## 865 2020-03-07 South West 0
## 866 2020-03-08 South West 0
## 867 2020-03-09 South West 0
## 868 2020-03-10 South West 0
## 869 2020-03-11 South West 1
## 870 2020-03-12 South West 0
## 871 2020-03-13 South West 0
## 872 2020-03-14 South West 1
## 873 2020-03-15 South West 0
## 874 2020-03-16 South West 0
## 875 2020-03-17 South West 2
## 876 2020-03-18 South West 2
## 877 2020-03-19 South West 4
## 878 2020-03-20 South West 3
## 879 2020-03-21 South West 6
## 880 2020-03-22 South West 7
## 881 2020-03-23 South West 8
## 882 2020-03-24 South West 7
## 883 2020-03-25 South West 9
## 884 2020-03-26 South West 11
## 885 2020-03-27 South West 13
## 886 2020-03-28 South West 21
## 887 2020-03-29 South West 18
## 888 2020-03-30 South West 23
## 889 2020-03-31 South West 23
## 890 2020-04-01 South West 21
## 891 2020-04-02 South West 23
## 892 2020-04-03 South West 30
## 893 2020-04-04 South West 42
## 894 2020-04-05 South West 32
## 895 2020-04-06 South West 34
## 896 2020-04-07 South West 39
## 897 2020-04-08 South West 47
## 898 2020-04-09 South West 24
## 899 2020-04-10 South West 46
## 900 2020-04-11 South West 43
## 901 2020-04-12 South West 23
## 902 2020-04-13 South West 27
## 903 2020-04-14 South West 24
## 904 2020-04-15 South West 32
## 905 2020-04-16 South West 29
## 906 2020-04-17 South West 33
## 907 2020-04-18 South West 25
## 908 2020-04-19 South West 31
## 909 2020-04-20 South West 26
## 910 2020-04-21 South West 26
## 911 2020-04-22 South West 23
## 912 2020-04-23 South West 17
## 913 2020-04-24 South West 19
## 914 2020-04-25 South West 15
## 915 2020-04-26 South West 27
## 916 2020-04-27 South West 13
## 917 2020-04-28 South West 17
## 918 2020-04-29 South West 15
## 919 2020-04-30 South West 26
## 920 2020-05-01 South West 6
## 921 2020-05-02 South West 7
## 922 2020-05-03 South West 10
## 923 2020-05-04 South West 17
## 924 2020-05-05 South West 14
## 925 2020-05-06 South West 19
## 926 2020-05-07 South West 16
## 927 2020-05-08 South West 6
## 928 2020-05-09 South West 11
## 929 2020-05-10 South West 5
## 930 2020-05-11 South West 8
## 931 2020-05-12 South West 7
## 932 2020-05-13 South West 7
## 933 2020-05-14 South West 6
## 934 2020-05-15 South West 4
## 935 2020-05-16 South West 4
## 936 2020-05-17 South West 6
## 937 2020-05-18 South West 4
## 938 2020-05-19 South West 6
## 939 2020-05-20 South West 1
## 940 2020-05-21 South West 9
## 941 2020-05-22 South West 6
## 942 2020-05-23 South West 6
## 943 2020-05-24 South West 3
## 944 2020-05-25 South West 8
## 945 2020-05-26 South West 11
## 946 2020-05-27 South West 5
## 947 2020-05-28 South West 10
## 948 2020-05-29 South West 7
## 949 2020-05-30 South West 3
## 950 2020-05-31 South West 2
## 951 2020-06-01 South West 7
## 952 2020-06-02 South West 2
## 953 2020-06-03 South West 7
## 954 2020-06-04 South West 2
## 955 2020-06-05 South West 2
## 956 2020-06-06 South West 1
## 957 2020-06-07 South West 3
## 958 2020-06-08 South West 3
## 959 2020-06-09 South West 0
## 960 2020-06-10 South West 1
## 961 2020-06-11 South West 2
## 962 2020-06-12 South West 2
## 963 2020-06-13 South West 2
## 964 2020-06-14 South West 0
## 965 2020-06-15 South West 2
## 966 2020-06-16 South West 2
## 967 2020-06-17 South West 0
## 968 2020-06-18 South West 0
## 969 2020-06-19 South West 0
## 970 2020-06-20 South West 2
## 971 2020-06-21 South West 0
## 972 2020-06-22 South West 1
## 973 2020-06-23 South West 1
## 974 2020-06-24 South West 1
## 975 2020-06-25 South West 0
## 976 2020-06-26 South West 3
## 977 2020-06-27 South West 0
## 978 2020-06-28 South West 0
## 979 2020-06-29 South West 1
## 980 2020-06-30 South West 0
## 981 2020-07-01 South West 0
## 982 2020-07-02 South West 0
## 983 2020-07-03 South West 0
## 984 2020-07-04 South West 0
## 985 2020-07-05 South West 1
## 986 2020-07-06 South West 0
## 987 2020-07-07 South West 0
## 988 2020-07-08 South West 2
## 989 2020-07-09 South West 0
## 990 2020-07-10 South West 1
## 991 2020-07-11 South West 0
## 992 2020-07-12 South West 0
## 993 2020-07-13 South West 1
## 994 2020-07-14 South West 0
## 995 2020-07-15 South West 0
## 996 2020-07-16 South West 0
## 997 2020-07-17 South West 1
## 998 2020-07-18 South West 0
## 999 2020-07-19 South West 0
## 1000 2020-07-20 South West 0
## 1001 2020-07-21 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Tuesday 21 Jul 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -13.9890 -4.9327 -0.4463 3.8564 8.8587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.533e+00 6.685e-02 67.81 <2e-16 ***
## note_lag 1.505e-05 6.973e-07 21.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 26.75576)
##
## Null deviance: 13578.2 on 81 degrees of freedom
## Residual deviance: 2271.7 on 80 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 93.066621 1.000015
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 81.460209 105.871939
## note_lag 1.000014 1.000016
Rsq(lag_mod)
## [1] 0.8326988
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.2.0
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.4.2 webshot_0.5.2 httr_1.4.2
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-8 zip_2.0.4 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.6 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.12.8 modelr_0.1.8 vctrs_0.3.2 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.15
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1